In recent years, wireless communication systems have been used to convey a variety of information between multiple locations. With digital communications, information is translated into a digital or binary form, referred to as bits, for communications purposes. The transmitter maps this bit stream into a modulated symbol stream, which is detected at the digital receiver and mapped back into bits and information.
In digital wireless communications, the radio environment presents many difficulties that impede successful communications. One difficulty is that the signal level can fade, because the signal may travel in multiple paths. As a result, signal images arrive at the receiver antenna out of phase. This type of fading is commonly referred to as Rayleigh fading or fast fading. When the signal fades, the signal-to-noise ratio becomes lower, causing a degradation in the communication link quality.
A second problem occurs when the multiple signal paths are much different in length. In this case, time dispersion occurs in which multiple fading signal images arrive at the receiver antenna at different times, giving rise to signal echoes. This causes intersymbol interference (ISI), where the echoes of one symbol interfere with subsequent symbols.
Raleigh fading can be mitigated by using diversity, such as antenna diversity, at the receiver. The signal is received on a plurality of antennas. Because the antennas have slightly different locations and/or antenna patterns, the fading levels on the antennas are different. In the receiver, these multiple antenna signals are combined either before or after signal detection using such techniques as maximal-ratio-combining, equal-gain-combining, and selective combining. These techniques are well known to those skilled in the art and can be found in standard textbooks, such as W. C. Y. Lee, Mobile Communications Engineering, New York: McGraw-Hill, 1982.
The time dispersion can be mitigated by using an equalizer. Common forms of equalization are provided by linear equalizers, decision-feedback equalizers, and maximum-likelihood sequence-estimation (MLSE) equalizers. A linear equalizer tries to undo the effects of the channel by filtering the received signal. A decision-feedback equalizer exploits previous symbol detections to cancel out the intersymbol interference from echoes of these previous symbols. Finally, an MLSE equalizer hypothesizes various transmitted symbol sequences and, with a model of the dispersive channel, determines which hypothesis best fits the received data. These equalization techniques are well known to those skilled in the art, and can be found in standard textbooks such as J. G. Proakis, Digital Communications, 2nd ed. New York: McGraw-Hill, 1989.
Of the three common equalization techniques, MLSE equalization has been considered preferable from a performance point of view. In the MLSE equalizer, all possible transmitted symbol sequences are considered. For each hypothetical sequence, the received signal samples are predicted using a model of the multipath channel. The difference between the predicted received signal samples and the actual received signal samples, referred to as the prediction error, gives an indication of how good a particular hypothesis is. The squared magnitude of the prediction error is used as a metric to evaluate a particular hypothesis. This metric is accumulated for different hypotheses for use in determining which hypotheses are better. This process is efficiently realized using the Viterbi algorithm, which is a form of dynamic programming.
Ideally, the diversity combining process and the equalization process should be combined in some optimal way. Recent research has shown that for MLSE equalization, diversity combining should be done within the equalizer. This research can be found in W. H. Sheen and G. L. Stuber, "MLSE equalization and decoding for multipath-fading channels," IEEE Trans. Commun., vol. 39, pp. 1455-1464, Oct. 1991; Q. Liu and Y. Wan "An adaptive maximum-likelihood sequence estimation receiver with dual diversity combining/selection," Ind. Symp. on Personal, Indoor and Mobile Radio Commun., Boston, Mass., pp. 245-249, Oct. 19-21, 1992, and Q. Liu and Y. Wan, "A unified MLSE detection technique for TDMA digital cellular radio," 43rd IEEE Vehicular Technology Conference, Seacaucus, N.J., pp. 265-268, May 18-20, 1993. In the above mentioned research, diversity combining is performed by adding together the magnitude squared prediction errors from different diversity channels when forming metrics.
The use of antenna arrays at base stations in a mobile communication systems has also been proposed as a technique for increasing capacity and performance. The most common approach for processing the information gathered by each antenna associated with a particular signal is based on direction of arrival (DOA) estimation followed by beamforming, i.e. combining the vector signal from the array to a scalar signal (spatial filtering) before detection. However, this approach does not fully exploit the spatial structure of the channel. A better way is to use an algorithm that is adaptive in the spatial domain and which also takes the quality that the transmitted signal has a finite alphabet (e.g., 0's and 1's) into account. Examples of such algorithms are the recently proposed iterative least squares with projections (ILSP) algorithm and the decoupled weighted least squares with projections (DWILSP) algorithm. The decoupled algorithm is similar to ILSP in performance, but is computationally cheaper.
Both ILSP and DWILSP are, in their original formulation, limited to use on frequency-flat (i.e., non time-dispersive) channels. However, in many mobile communication systems, the channel cannot be modelled as frequency-flat. To treat time-dispersive channels, extensions to the iterative least squares approaches have also been presented. These algorithms are unfortunately quite complex, both regarding computational aspects and detection procedures involved.
Another drawback associated with these conventional algorithms is their requirement of precise synchronization. Although the DWILSP algorithm can be used to process signals received from unsynchronized cochannel users, synchronization with the signal of interest is still assumed, i.e., the signal of interest is assumed to be sampled correctly in accordance with the symbol timing. In practice, this assumption may not hold true, since perfect symbol timing is difficult to achieve. For example, in certain types of systems, e.g., time division multiple access (TDMA) systems which use short transmission bursts, proper sample timing is extremely difficult to guarantee. Thus, as will be illustrated in the simulations performed by Applicants and described below, the conventional DWILSP algorithm suffers significant degradation (e.g., increased bit error rate) when timing errors are introduced into the sampled signal.
Several techniques have been proposed which use oversampling, i.e., taking more than one time discrete sample during each symbol interval, to handle the problems associated with unsynchronized signals. The DWILSP algorithm, however, is designed to use only one sample per symbol interval and, therefore, is not amenable to these types of solutions.
Accordingly, it would be desirable to provide a technique for estimating symbols using the DWILSP algorithm from unsynchronized signals sampled at the symbol rate. Moreover, it would also be desirable to use the DWILSP algorithm to obtain improved diversity combining.